{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ggplot: (285210741)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CFgCAJAlZAACSJGQBAEiSkAUAIElCFgCAJAlZAACSJGQBAEiSkAUAIElCFgCAJAlZAACS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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1103e20d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(mtcars, aes(x='factor(cyl)')) + geom_bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
